In this episode, we examine the timeless question of “free will”: what constitutes it, what is meant by it, what ought to be meant by it, and, of course, whether we have it at all. We start with the neuroscientist Robert Sapolsky who begins to deflate the widely held intuition and assumption of “libertarian free will” by drawing out a mechanistic and determined description of the universe. We then hear from the philosopher who has long been Sam’s intellectual wrestling opponent on this subject, Daniel Dennett. Dennett and Sam spar about definitional and epistemological frameworks of what Dennett insists is “free will,” and what Sam contends could never be. The author and physicist Sean Carroll then engages Sam with more attempts to find a philosophically defensible notion of free will by leaning on the unknowable nature of the universe revealed by quantum mechanics. We then listen in on Sam’s engagement with the mathematician and author Judea Pearl who focuses on matters of causation to tease out a freedom of will. After a historical review of Princess Elizabeth’s famous exchanges with Rene Descartes, we hear from the biologist Jerry Coyne, who firmly agrees with Sam that a deterministic picture of reality leaves absolutely no room for anything like free will. We then hear from the curiously entertaining mind of comedian and producer Ricky Gervais who was thinking about free will while taking a bath when he decided to phone Sam. We conclude with Sam’s own response to concerns that an erasure of free will inevitably result in fatalism, loss of meaning, and passive defeat. Sam insists that the loss of free will actually pushes us in the opposite direction where we begin to see hatred and vengeance as incoherent and start to connect with a deeper and truer sense of genuine compassion.   About the Series Filmmaker Jay Shapiro has produced The Essential Sam Harris, a new series of audio documentaries exploring the major topics that Sam has focused on over the course of his career. Each episode weaves together original analysis, critical perspective, and novel thought experiments with some of the most compelling exchanges from the Making Sense archive. Whether you are new to a particular topic, or think you have your mind made up about it, we think you’ll find this series fascinating.

Judea Pearl is a professor at UCLA and a winner of the Turing Award, that’s generally recognized as the Nobel Prize of computing. He is one of the seminal figures in the field of artificial intelligence, computer science, and statistics. He has developed and championed probabilistic approaches to AI, including Bayesian Networks and profound ideas in causality in general. These ideas are important not just for AI, but to our understanding and practice of science. But in the field of AI, the idea of causality, cause and effect, to many, lies at the core of what is currently missing and what must be developed in order to build truly intelligent systems. For this reason, and many others, his work is worth returning to often. This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts or support it on Patreon. This episode is presented by Cash App. Download it (App Store, Google Play), use code “LexPodcast”.  Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time. 00:00 – Introduction 03:18 – Descartes and analytic geometry 06:25 – Good way to teach math 07:10 – From math to engineering 09:14 – Does God play dice? 10:47 – Free will 11:59 – Probability 22:21 – Machine learning 23:13 – Causal Networks 27:48 – Intelligent systems that reason with causation 29:29 – Do(x) operator 36:57 – Counterfactuals 44:12 – Reasoning by Metaphor 51:15 – Machine learning and causal reasoning 53:28 – Temporal aspect of causation 56:21 – Machine learning (continued) 59:15 – Human-level artificial intelligence 1:04:08 – Consciousness 1:04:31 – Concerns about AGI 1:09:53 – Religion and robotics 1:12:07 – Daniel Pearl 1:19:09 – Advice for students 1:21:00 – Legacy

To say that event A causes event B is to not only make a claim about our actual world, but about other possible worlds — in worlds where A didn’t happen but everything else was the same, B would not have happened. This leads to an obvious difficulty if we want to infer causes from sets of data — we generally only have data about the actual world. Happily, there are ways around this difficulty, and the study of causal relations is of central importance in modern social science and artificial intelligence research. Judea Pearl has been the leader of the “causal revolution,” and we talk about what that means and what questions remain unanswered.Support Mindscape on Patreon.Judea Pearl received a Ph.D. in electrical engineering from the Polytechnic Institute of Brooklyn. He is currently a professor of computer science and statistics and director of the Cognitive Systems Laboratory at UCLA. He is a founding editor of the Journal of Causal Inference. Among his awards are the Lakatos Award in the philosophy of science, The Allen Newell Award from the Association for Computing Machinery, the Benjamin Franklin Medal, the Rumelhart Prize from the Cognitive Science Society, the ACM Turing Award, and the Grenander Prize from the American Mathematical Society. He is the co-author (with Dana MacKenzie) of The Book of Why: The New Science of Cause and Effect.Web siteGoogle Scholar publicationsWikipediaAmazon author pageTwitterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this conversation, Judea Pearl, the godfather of modern causal inference, dives into the complexities of causal reasoning and artificial intelligence. He discusses the influence of human biases on AI's decision-making and the potential future of large language models in scientific experimentation. Pearl emphasizes the importance of bridging causal reasoning with statistical education, using malaria as a case study. He also shares insights on the challenges of implementing evidence-based frameworks in education, advocating for a unified understanding of causality across disciplines.

Judea Pearl, famous researcher known for Bayesian networks and statistical formalization of causality, discusses the need for a causal model and challenges machine learning's limitation to statistics-level reasoning. They explore surprising changes in perspective on causal queries and GPT capabilities, levels of causation in AI, and ethical implications in the shift towards general AI.